147,594 research outputs found
Evolutionary multi-stage financial scenario tree generation
Multi-stage financial decision optimization under uncertainty depends on a
careful numerical approximation of the underlying stochastic process, which
describes the future returns of the selected assets or asset categories.
Various approaches towards an optimal generation of discrete-time,
discrete-state approximations (represented as scenario trees) have been
suggested in the literature. In this paper, a new evolutionary algorithm to
create scenario trees for multi-stage financial optimization models will be
presented. Numerical results and implementation details conclude the paper
An Optimization Model for Single-Warehouse Multi-Agents Distribution Network Problems under Varying of Transportation Facilities: A Case Study
The transportation cost of goods is the highest day-to-day operational cost associated with the
food industry sector. A company may be able to reduce logistics cost and simultaneously improve service
level by optimizing of distribution network. In reality, a company faces problems considering capacitated
transportation facilities and time constraint of delivery. In this paper, we develop a new model of order
fulfillment physical distribution to minimize transportation cost under limited of transportation facilities.
The first step is defined problem description. After that, we formulate a integer linear programming model
for the single-warehouse, multiple-agents considering varying of transportation facilities in multi-period
shipment planning. We analyze problems faced by company when should decide policy of distribution due to
varying of transportation facilities in volume, type of vehicle, delivery cost, lead time and ownership of
facilities. We assumed transportation costs are modeled with a linear term in the objective function. Then,
we solve the model with Microsoft Excel Solver 8.0 Version. Finally, we analyze the results with considering
amount of transportation facilities, volume usage and total transportation cost.
Keywords: physical distribution, shipment planning, integer linear programming, transportation cost,
transportation facilities
User-friendly Support for Common Concepts in a Lightweight Verifier
Machine verification of formal arguments can only increase our confidence in the correctness of those arguments, but the costs of employing machine verification still outweigh the benefits for some common kinds of formal reasoning activities. As a result, usability is becoming increasingly important in the design of formal verification tools. We describe the "aartifact" lightweight verification system, designed for processing formal arguments involving basic, ubiquitous mathematical concepts. The system is a prototype for investigating potential techniques for improving the usability of formal verification systems. It leverages techniques drawn both from existing work and from our own efforts. In addition to a parser for a familiar concrete syntax and a mechanism for automated syntax lookup, the system integrates (1) a basic logical inference algorithm, (2) a database of propositions governing common mathematical concepts, and (3) a data structure that computes congruence closures of expressions involving relations found in this database. Together, these components allow the system to better accommodate the expectations of users interested in verifying formal arguments involving algebraic and logical manipulations of numbers, sets, vectors, and related operators and predicates. We demonstrate the reasonable performance of this system on typical formal arguments and briefly discuss how the system's design contributed to its usability in two case studies
A nonmonotone GRASP
A greedy randomized adaptive search procedure (GRASP) is an itera-
tive multistart metaheuristic for difficult combinatorial optimization problems. Each
GRASP iteration consists of two phases: a construction phase, in which a feasible
solution is produced, and a local search phase, in which a local optimum in the
neighborhood of the constructed solution is sought. Repeated applications of the con-
struction procedure yields different starting solutions for the local search and the
best overall solution is kept as the result. The GRASP local search applies iterative
improvement until a locally optimal solution is found. During this phase, starting from
the current solution an improving neighbor solution is accepted and considered as the
new current solution. In this paper, we propose a variant of the GRASP framework that
uses a new “nonmonotone” strategy to explore the neighborhood of the current solu-
tion. We formally state the convergence of the nonmonotone local search to a locally
optimal solution and illustrate the effectiveness of the resulting Nonmonotone GRASP
on three classical hard combinatorial optimization problems: the maximum cut prob-
lem (MAX-CUT), the weighted maximum satisfiability problem (MAX-SAT), and
the quadratic assignment problem (QAP)
Air handling system optimisation
Often each floor of a building has an air handling system consisting of a plant room, ducting and equipment such as fans and heaters. The cost of the system consists of the capital cost of the equipment and the operating cost to satisfy the thermal loads. An efficient method is required for evaluating the operating costs when the configuration of the system is specified and the thermal loads are known. The operating cost of a particular configuration are obtained by solving a nonlinear program. The method is efficient since it consists of solving a sequence of single period models
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